• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.4
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• pp.115-125
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• 2022

In today's era when the concept of a lifelong job has disappeared, starting a business is an essential consideration for university students not only as an alternative factor in finding employment, but also from the perspective of the entire life. Today, most universities in Korea are operating entrepreneurship education programs, such as entrepreneurship classes as a curriculum, and start-up clubs as a non-curricular program to foster entrepreneurship among university students. In previous studies, entrepreneurship is a factor influencing the entrepreneurship intention. The purpose of this study is to empirically examine the effects of university students' entrepreneurship and the entrepreneurship intention through a entrepreneurship education program (participation in a start-up club, taking an entrepreneurship course) on entrepreneurship behavior. There are some empirical studies on whether entrepreneurship education programs such as participation in startup contests affect the entrepreneurship intention of university students, but not much compared to their importance. It is difficult to find an empirical study examining the effects of entrepreneurship and start-up education programs on entrepreneurship intention and entrepreneurship behavior in domestic and foreign studies. Therefore, in this study, one domestic university that operates a start-up club and a entrepreneurship course was selected and the online questionnaire was distributed to all current students, and the collected 127 questionnaires were used for empirical analysis As a result of the study, first, it was confirmed that initiative and risk-taking, which are sub-factors of entrepreneurship of university students, had a significant positive effect on entrepreneurial intention, respectively, and that innovation did not have a significant positive effect. Second, it was confirmed that university students' participation in a start-up club, a sub-factor of the start-up education program, had a significant positive effect on their entrepreneurial intention, and that taking a start-up course did not have a significant positive effect. Third, it was confirmed that the entrepreneurial intention of university students had a significant positive effect on entrepreneurship behavior. Fourth, it was confirmed that the entrepreneurial intention had a mediating effect between each of the factors of risk-taking, and participation in a start-up club and entrepreneurial behavior. This study suggests that university students can increase their risk-taking, increase their entrepreneurial intention by participating in a startup club, and reach a entrepreneurial behavior through this as a medium.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.53-69
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• 2011

Vision and voice-based technologies are commonly utilized for human-robot interaction. But it is widely recognized that the performance of vision and voice-based interaction systems is deteriorated by a large margin in the real-world situations due to environmental and user variances. Human users need to be very cooperative to get reasonable performance, which significantly limits the usability of the vision and voice-based human-robot interaction technologies. As a result, touch screens are still the major medium of human-robot interaction for the real-world applications. To empower the usability of robots for various services, alternative interaction technologies should be developed to complement the problems of vision and voice-based technologies. In this paper, we propose the use of accelerometer-based gesture interface as one of the alternative technologies, because accelerometers are effective in detecting the movements of human body, while their performance is not limited by environmental contexts such as lighting conditions or camera's field-of-view. Moreover, accelerometers are widely available nowadays in many mobile devices. We tackle the problem of classifying acceleration signal patterns of 26 English alphabets, which is one of the essential repertoires for the realization of education services based on robots. Recognizing 26 English handwriting patterns based on accelerometers is a very difficult task to take over because of its large scale of pattern classes and the complexity of each pattern. The most difficult problem that has been undertaken which is similar to our problem was recognizing acceleration signal patterns of 10 handwritten digits. Most previous studies dealt with pattern sets of 8~10 simple and easily distinguishable gestures that are useful for controlling home appliances, computer applications, robots etc. Good features are essential for the success of pattern recognition. To promote the discriminative power upon complex English alphabet patterns, we extracted 'motion trajectories' out of input acceleration signal and used them as the main feature. Investigative experiments showed that classifiers based on trajectory performed 3%~5% better than those with raw features e.g. acceleration signal itself or statistical figures. To minimize the distortion of trajectories, we applied a simple but effective set of smoothing filters and band-pass filters. It is well known that acceleration patterns for the same gesture is very different among different performers. To tackle the problem, online incremental learning is applied for our system to make it adaptive to the users' distinctive motion properties. Our system is based on instance-based learning (IBL) where each training sample is memorized as a reference pattern. Brute-force incremental learning in IBL continuously accumulates reference patterns, which is a problem because it not only slows down the classification but also downgrades the recall performance. Regarding the latter phenomenon, we observed a tendency that as the number of reference patterns grows, some reference patterns contribute more to the false positive classification. Thus, we devised an algorithm for optimizing the reference pattern set based on the positive and negative contribution of each reference pattern. The algorithm is performed periodically to remove reference patterns that have a very low positive contribution or a high negative contribution. Experiments were performed on 6500 gesture patterns collected from 50 adults of 30~50 years old. Each alphabet was performed 5 times per participant using $Nintendo{(R)}$ $Wii^{TM}$ remote. Acceleration signal was sampled in 100hz on 3 axes. Mean recall rate for all the alphabets was 95.48%. Some alphabets recorded very low recall rate and exhibited very high pairwise confusion rate. Major confusion pairs are D(88%) and P(74%), I(81%) and U(75%), N(88%) and W(100%). Though W was recalled perfectly, it contributed much to the false positive classification of N. By comparison with major previous results from VTT (96% for 8 control gestures), CMU (97% for 10 control gestures) and Samsung Electronics(97% for 10 digits and a control gesture), we could find that the performance of our system is superior regarding the number of pattern classes and the complexity of patterns. Using our gesture interaction system, we conducted 2 case studies of robot-based edutainment services. The services were implemented on various robot platforms and mobile devices including $iPhone^{TM}$. The participating children exhibited improved concentration and active reaction on the service with our gesture interface. To prove the effectiveness of our gesture interface, a test was taken by the children after experiencing an English teaching service. The test result showed that those who played with the gesture interface-based robot content marked 10% better score than those with conventional teaching. We conclude that the accelerometer-based gesture interface is a promising technology for flourishing real-world robot-based services and content by complementing the limits of today's conventional interfaces e.g. touch screen, vision and voice.